# -*- coding: utf-8 -*-
# ---------------------------------------------------------------------
# Part of the following code in this file refs to torchvision
# BSD 3-Clause License
#
# Copyright (c) Soumith Chintala 2016,
# All rights reserved.
# ---------------------------------------------------------------------
import collections.abc
import os
import xml.etree.ElementTree as ET
import cv2
import numpy as np
from .meta_vision import VisionDataset
[文档]class PascalVOC(VisionDataset):
r"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Dataset."""
supported_order = (
"image",
"boxes",
"boxes_category",
"mask",
"info",
)
def __init__(self, root, image_set, *, order=None):
if ("boxes" in order or "boxes_category" in order) and "mask" in order:
raise ValueError(
"PascalVOC only supports boxes & boxes_category or mask, not both."
)
super().__init__(root, order=order, supported_order=self.supported_order)
if not os.path.isdir(self.root):
raise RuntimeError("Dataset not found or corrupted.")
self.image_set = image_set
image_dir = os.path.join(self.root, "JPEGImages")
if "boxes" in order or "boxes_category" in order:
annotation_dir = os.path.join(self.root, "Annotations")
splitdet_dir = os.path.join(self.root, "ImageSets/Main")
split_f = os.path.join(splitdet_dir, image_set.rstrip("\n") + ".txt")
with open(os.path.join(split_f), "r") as f:
self.file_names = [x.strip() for x in f.readlines()]
self.images = [os.path.join(image_dir, x + ".jpg") for x in self.file_names]
self.annotations = [
os.path.join(annotation_dir, x + ".xml") for x in self.file_names
]
assert len(self.images) == len(self.annotations)
elif "mask" in order:
if "aug" in image_set:
mask_dir = os.path.join(self.root, "SegmentationClass_aug")
else:
mask_dir = os.path.join(self.root, "SegmentationClass")
splitmask_dir = os.path.join(self.root, "ImageSets/Segmentation")
split_f = os.path.join(splitmask_dir, image_set.rstrip("\n") + ".txt")
with open(os.path.join(split_f), "r") as f:
self.file_names = [x.strip() for x in f.readlines()]
self.images = [os.path.join(image_dir, x + ".jpg") for x in self.file_names]
self.masks = [os.path.join(mask_dir, x + ".png") for x in self.file_names]
assert len(self.images) == len(self.masks)
else:
raise NotImplementedError
self.img_infos = dict()
def __getitem__(self, index):
target = []
for k in self.order:
if k == "image":
image = cv2.imread(self.images[index], cv2.IMREAD_COLOR)
target.append(image)
elif k == "boxes":
anno = self.parse_voc_xml(ET.parse(self.annotations[index]).getroot())
boxes = [obj["bndbox"] for obj in anno["annotation"]["object"]]
# boxes type xyxy
boxes = [
(bb["xmin"], bb["ymin"], bb["xmax"], bb["ymax"]) for bb in boxes
]
boxes = np.array(boxes, dtype=np.float32).reshape(-1, 4)
target.append(boxes)
elif k == "boxes_category":
anno = self.parse_voc_xml(ET.parse(self.annotations[index]).getroot())
boxes_category = [obj["name"] for obj in anno["annotation"]["object"]]
boxes_category = [
self.class_names.index(bc) + 1 for bc in boxes_category
]
boxes_category = np.array(boxes_category, dtype=np.int32)
target.append(boxes_category)
elif k == "mask":
if "aug" in self.image_set:
mask = cv2.imread(self.masks[index], cv2.IMREAD_GRAYSCALE)
else:
mask = cv2.imread(self.masks[index], cv2.IMREAD_COLOR)
mask = self._trans_mask(mask)
mask = mask[:, :, np.newaxis]
target.append(mask)
elif k == "info":
info = self.get_img_info(index, image)
info = [info["height"], info["width"], info["file_name"]]
target.append(info)
else:
raise NotImplementedError
return tuple(target)
def __len__(self):
return len(self.images)
def get_img_info(self, index, image=None):
if index not in self.img_infos:
if image is None:
image = cv2.imread(self.images[index], cv2.IMREAD_COLOR)
self.img_infos[index] = dict(
height=image.shape[0],
width=image.shape[1],
file_name=self.file_names[index],
)
return self.img_infos[index]
def _trans_mask(self, mask):
label = np.ones(mask.shape[:2]) * 255
for i in range(len(self.class_colors)):
b, g, r = self.class_colors[i]
label[
(mask[:, :, 0] == b) & (mask[:, :, 1] == g) & (mask[:, :, 2] == r)
] = i
return label.astype(np.uint8)
def parse_voc_xml(self, node):
voc_dict = {}
children = list(node)
if children:
def_dic = collections.defaultdict(list)
for dc in map(self.parse_voc_xml, children):
for ind, v in dc.items():
def_dic[ind].append(v)
if node.tag == "annotation":
def_dic["object"] = [def_dic["object"]]
voc_dict = {
node.tag: {
ind: v[0] if len(v) == 1 else v for ind, v in def_dic.items()
}
}
if node.text:
text = node.text.strip()
if not children:
voc_dict[node.tag] = text
return voc_dict
class_names = (
"aeroplane",
"bicycle",
"bird",
"boat",
"bottle",
"bus",
"car",
"cat",
"chair",
"cow",
"diningtable",
"dog",
"horse",
"motorbike",
"person",
"pottedplant",
"sheep",
"sofa",
"train",
"tvmonitor",
)
class_colors = [
[0, 0, 0], # background
[0, 0, 128],
[0, 128, 0],
[0, 128, 128],
[128, 0, 0],
[128, 0, 128],
[128, 128, 0],
[128, 128, 128],
[0, 0, 64],
[0, 0, 192],
[0, 128, 64],
[0, 128, 192],
[128, 0, 64],
[128, 0, 192],
[128, 128, 64],
[128, 128, 192],
[0, 64, 0],
[0, 64, 128],
[0, 192, 0],
[0, 192, 128],
[128, 64, 0],
]